Volcanogenic massive sulphide exploration in glaciated terrain using till geochemistry and indicator minerals
Bibliographic record
Abstract
Volcanogenic massive sulphide (VMS) deposits are a significant exploration target in Canada because they account for 27% of Canadian Cu, 49% of Zn, 20% of Pb, 40% of Ag, and 3% of Au production (http://www.nrcan.gc.ca/minerals-metals/home). Over 97% of Canada's land mass was covered by glaciers during the Quaternary and as a result drift prospecting using till geochemistry and indicator minerals is an important exploration method for VMS deposits in Canada. The application of till geochemical methods to VMS exploration in Canada has a 50+ year history (e.g., Drieimanis 1958, 1960; Fortescue and& Hornbrook 1969; Shilts 1975; Hoffman and Woods 1991; Kaszycki et al. 1996; Parkhill and& Doiron 2003). In the past 10 years, indicator mineral recovered from glacial sediments have also been used to explore for VMS in the glaciated terrain of Canada. This abstract provides an overview of till geochemical and indicator mineral methods that can be used for VMS exploration. Topics addressed include appropriate size fractions of till to analyze, processing and analytical techniques, VMS pathfinder elements and indicator minerals, as well as case histories from different regions across Canada. Much of the information about VMS till geochemical methods summarized here is from a detailed review of the application of till geochemical methods to VMS exploration by McClenaghan and Peter (in press).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".